Generalized Langevin equations (GLEs) are stochastic integro-differential equations commonly used as models in non-equilibrium statistical mechanics to describe the dynamics of a particle coupled to a heat bath. From modeling point of view, it is often desirable to derive effective mathematical models, in the form of stochastic differential equations (SDEs), to capture the essential dynamics of the systems. In this talk, we consider effective SDEs describing the behavior of a large class of generalized Langevin systems in the limits when natural time scales become very small. It turns out that additional drift terms, called noise-induced drifts, appear in the effective SDEs. We discuss recent progress on the phenomena of noise-induced drift in these systems. This is joint work with Jan Wehr and Maciej Lowenstein.

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard current methods rely on algorithmic approaches: by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.

Albanova, Stockholm’s center for Physics, Astronomy and Biotechnology cordially organizes a panel discussion about this year’s Nobel Prize in Physics, followed by a social gathering with drinks and snacks.

The accurate measurement of microscopic force fields is crucial in many branches of science and technology, from biophotonics and mechanobiology to microscopy and optomechanics. These forces are often probed by analysing their influence on the motion of Brownian particles. Here we introduce a powerful algorithm for microscopic force reconstruction via maximum-likelihood-estimator analysis (FORMA) to retrieve the force field acting on a Brownian particle from the analysis of its displacements. FORMA estimates accurately the conservative and non-conservative components of the force field with important advantages over established techniques, being parameter-free, requiring ten-fold less data and executing orders-of-magnitude faster. We demonstrate FORMA performance using optical tweezers, showing how, outperforming other available techniques, it can identify and characterise stable and unstable equilibrium points in generic force fields. Thanks to its high performance, FORMA can accelerate the development of microscopic and nanoscopic force transducers for physics, biology and engineering.

The presence of a delay between sensing and reacting to a signal can determine the long-term behavior of autonomous agents whose motion is intrinsically noisy.
In a previous work [M. Mijalkov, A. McDaniel, J. Wehr, and G. Volpe, Phys. Rev. X 6, 011008 (2016)], we have shown that sensorial delay can alter the drift and the position probability distribution of an autonomous agent whose speed depends on the illumination intensity it measures. Here, using theory, simulations, and experiments with a phototactic robot, we generalize this effect to an agent for which both speed and rotational diffusion depend on the illumination intensity and are subject to two independent sensorial delays. We show that both the drift and the probability distribution are influenced by the presence of these sensorial delays. In particular, the radial drift may have positive as well as negative sign, and the position probability distribution peaks in different regions depending on the delay.
Furthermore, the presence of multiple sensorial delays permits us to explore the role of the interaction between them.

Abstract: An optically trapped absorbing microsphere in a sub-critical mixture rotates around the optical trap thanks to diffusiophoretic propulsion, which can be controlled by adjusting the optical power, the temperature, and the criticality of the mixture.

Three questions for Giovanni Volpe, appointed Docent in Physics at the Faculty of Science, University of Gothenburg.

Interview by: Linnéa Magnusson
Photo by: Malin Arnesson

What is your research about?

“I am conducting research in several different areas. Part of my work concerns artificial micro swimmers. In simple terms, this is about biological and artificial objects of microscopic size that can get around by themselves and counteract microorganisms. Research on micro swimmers involves many possibilities within basic science, nanoscience and nanotechnology.

“I am collaborating with Karolinska Institutet on a project that deals with neurodegenerative diseases such as Alzheimer’s, Parkinson’s and ALS (amyotrophic lateral sclerosis). We have developed software that serves as a toolkit, helping us to detect these diseases at an early stage.

“Another project deals with optical trapping and optical manipulation. Using optical tweezers, I can measure microscopic forces, for example.

“Finally, I am also working on a project that involves managing the challenges of condensed matter physics – in other words, matter and processes at the atomic level. With the help of machine learning, we can handle complex algorithms.”

What can society learn from your research?

“I hope that our work with micro swimmers can become a foundation on which we can build, so that in the future we can use them in real life. For example, this could involve cleaning contaminated soil or developing what are known as chiral drugs – medications that are more selective and more controllable and that have fewer side effects. It is to be hoped that our work in neuroscience will lead us to quickly detect and treat neurodegenerative diseases.”

What do you think is most exciting about the future?

“What is most exciting is the possibility of using artificial intelligence to solve physical and medical problems. In the future we will go from people developing and testing ideas to have data and systems under investigation speak for themselves.

Description: This course will review the theoretical underpinnings of optical trapping and optical manipulation; a review of recent applications; and provide a hands-on tutorial on the use of computational methods to simulate optical trapping and the motion of optically trapped particles.